陈鑫, 徐赛, 陆华忠, 等. 基于ZEMAX的柚果内部品质光谱无损检测光路仿真与试验[J]. 华南农业大学学报, 2024, 45(4): 618-623. doi: 10.7671/j.issn.1001-411X.202310002
    引用本文: 陈鑫, 徐赛, 陆华忠, 等. 基于ZEMAX的柚果内部品质光谱无损检测光路仿真与试验[J]. 华南农业大学学报, 2024, 45(4): 618-623. doi: 10.7671/j.issn.1001-411X.202310002
    CHEN Xin, XU Sai, LU Huazhong, et al. Optical path simulation and experiment for spectral nondestructive detection of pomelo fruit internal quality based on ZEMAX[J]. Journal of South China Agricultural University, 2024, 45(4): 618-623. doi: 10.7671/j.issn.1001-411X.202310002
    Citation: CHEN Xin, XU Sai, LU Huazhong, et al. Optical path simulation and experiment for spectral nondestructive detection of pomelo fruit internal quality based on ZEMAX[J]. Journal of South China Agricultural University, 2024, 45(4): 618-623. doi: 10.7671/j.issn.1001-411X.202310002

    基于ZEMAX的柚果内部品质光谱无损检测光路仿真与试验

    Optical path simulation and experiment for spectral nondestructive detection of pomelo fruit internal quality based on ZEMAX

    • 摘要:
      目的 解决柚果Citrus maxima在无损检测中采集的光谱信息较弱、柚果内部品质检测不准确的问题。
      方法 采用ZEMAX软件对光谱无损检测光路进行光学仿真,并搭建实物装置进行测试与验证。为进一步验证该光路对柚果内部品质的无损检测效果,采集400~1 100 nm波段132个柚果光谱数据,并理化测定其可溶性固形物含量进行训练建模和测试验证。
      结果 最优光路参数为:灯珠数量为7、柚果与光源间距为2 cm,探测器与柚果间距为0.5 cm。通过Savitzky-Golay (SG)平滑和标准正态变换(Standard normal variate,SNV)预处理,竞争自适应重加权采样(Competitive adaptive reweighted sampling,CARS)特征选择,建立柚果糖度偏最小二乘回归(Partial least squares regression,PLSR)预测模型,训练集R2和RMSE分别为0.81和0.85,测试集R2和RMSE分别为0.81和0.89。
      结论 本研究得到的柚果光谱无损检测光路参数可用于柚果内部品质的无损检测,检测效果较好,也为其他大尺寸水果内部无损检测提供了参考。

       

      Abstract:
      Objective This study is aimed to address the issue that the spectral information collected in the non-destructive testing of pomelo fruit (Citrus maxima) is weak, and the detected internal quality of pomelo fruit is not accurate.
      Method The optical simulation of the optical path of spectral nondestructive testing was carried out by using ZEMAX software, and tested and verified by the physical device. In order to further verify the nondestructive detection effect of the optical path on the internal quality of pomelo fruit, the spectral data of 132 pomelo fruits in 400−1 100 nm band were collected, and their soluble solid contents were determined physically and chemically for training modeling and testing verification.
      Result The optimal parameters of the optical path were as following: The number of lamp beads was 7, the distance between the fruit and the light source was 2 cm, and the distance between the detector and the fruit was 0.5 cm. Through Savitzky-Golay (SG) smoothing and standard normal variate (SNV) preprocessing, competitive adaptive reweighted sampling (CARS) feature selection, a partial least squares regression (PLSR) prediction model was established. The R2 and RMSE of the training set were 0.81 and 0.85, and those of the test set were 0.81 and 0.89, respectively.
      Conclusion The optical path parameters of pomelo fruit obtained in this study are applicable for non-destructive detection of pomelo fruit internal quality, yielding satisfactory detection results. The results provide a reference for the internal non-destructive testing of other large fruits.

       

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